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   "source": [
    "## Pandas Profiling: NASA Meteorites example\n",
    "Source of data: https://data.nasa.gov/Space-Science/Meteorite-Landings/gh4g-9sfh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The autoreload instruction reloads modules automatically before code execution, which is helpful for the update below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make sure that we have the latest version of pandas-profiling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "!{sys.executable} -m pip install -U pandas-profiling[notebook]\n",
    "!jupyter nbextension enable --py widgetsnbextension"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You might want to restart the kernel now."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "import requests\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import pandas_profiling\n",
    "from pandas_profiling.utils.cache import cache_file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load and prepare example dataset\n",
    "We add some fake variables for illustrating pandas-profiling capabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_name = cache_file(\n",
    "    \"meteorites.csv\",\n",
    "    \"https://data.nasa.gov/api/views/gh4g-9sfh/rows.csv?accessType=DOWNLOAD\",\n",
    ")\n",
    "    \n",
    "df = pd.read_csv(file_name)\n",
    "    \n",
    "# Note: Pandas does not support dates before 1880, so we ignore these for this analysis\n",
    "df['year'] = pd.to_datetime(df['year'], errors='coerce')\n",
    "\n",
    "# Example: Constant variable\n",
    "df['source'] = \"NASA\"\n",
    "\n",
    "# Example: Boolean variable\n",
    "df['boolean'] = np.random.choice([True, False], df.shape[0])\n",
    "\n",
    "# Example: Mixed with base types\n",
    "df['mixed'] = np.random.choice([1, \"A\"], df.shape[0])\n",
    "\n",
    "# Example: Highly correlated variables\n",
    "df['reclat_city'] = df['reclat'] + np.random.normal(scale=5,size=(len(df)))\n",
    "\n",
    "# Example: Duplicate observations\n",
    "duplicates_to_add = pd.DataFrame(df.iloc[0:10])\n",
    "duplicates_to_add[u'name'] = duplicates_to_add[u'name'] + \" copy\"\n",
    "\n",
    "df = df.append(duplicates_to_add, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inline report without saving object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "report = df.profile_report(sort='None', html={'style':{'full_width': True}}, progress_bar=False)\n",
    "report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save report to file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "profile_report = df.profile_report(html={'style': {'full_width': True}})\n",
    "profile_report.to_file(\"/tmp/example.html\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### More analysis (Unicode) and Print existing ProfileReport object inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile_report = df.profile_report(explorative=True, html={'style': {'full_width': True}})\n",
    "profile_report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Notebook Widgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile_report.to_widgets()"
   ]
  }
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